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Carbon dioxide emission prediction method

A technology of carbon dioxide and prediction methods, which is applied in the fields of prediction, instruments, and technical management, etc., can solve the problems of easy local optimization of calculation results, long training time, slow convergence speed, etc., and achieve excellent global search ability and high-efficiency computing performance Effect

Pending Publication Date: 2018-11-20
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

However, the neural network also has certain shortcomings, such as: slow convergence speed, long training time, calculation results are easy to fall into local optimum, and "over-fitting" phenomenon is prone to occur

Method used

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  • Carbon dioxide emission prediction method
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  • Carbon dioxide emission prediction method

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Embodiment Construction

[0033] The embodiments will be described in detail below in conjunction with the accompanying drawings.

[0034] (1) Gray prediction model GM(1,1)

[0035] The GM(1,1) model is the most commonly used gray model, which is composed of a first-order differential equation that only contains a single variable. The model is simple to calculate and has obvious advantages in predicting small sample data with irregular distribution. The specific modeling process of GM(1,1) is as follows:

[0036] let x (0) For the original data sequence:

[0037] x (0) =[x (0) (1),x (0) (2),...,x (0) (n)] (1)

[0038] For the original data sequence x (0) Make a cumulative generation to get the sequence x (1) :

[0039]

[0040] In the formula,

[0041] x (1) (k) The sequence satisfies the following first-order linear differential equation model:

[0042]

[0043] In the formula, a and u are parameters to be estimated.

[0044] According to the derivative definition, there are:

...

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Abstract

The invention belongs to the technical field of carbon emission prediction, and in particular relates to a carbon dioxide emission prediction method comprising the steps of collecting data including the historical CO2 emission, population, GDP per capita, urbanization rate, secondary industry added value proportion, energy consumption structure, energy strength, overall coal consumption, carbon emission strength and total export-import volume; performing non-dimensionalization on the data, computing a gray association degree between each piece of data and the CO2 emission, and screening CO2 emission influence factor indexes input by the model according to the gray association degrees to achieve feature dimension reduction; using a gray prediction model GM(1,1) to predict the screened CO2 emission influence factor index; and using predicted values of the CO2 emission influence factors to serve as model inputs, and then using an improved shuffled frog leaping algorithm to optimize a least square support vector machine model for predicting the CO2 emission. The method provide by the invention has efficient computing performance and excellent global searching ability.

Description

technical field [0001] The invention belongs to the technical field of carbon emission forecasting, and in particular relates to a carbon dioxide emission forecasting method. Background technique [0002] In recent years, for the prediction of carbon dioxide emissions, most of the traditional prediction methods are used, such as: time series method, regression analysis method, gray prediction method, etc. These methods have certain deficiencies, and the prediction accuracy needs to be improved, while the artificial intelligence prediction method In the face of complex nonlinear sequences, it shows strong advantages and achieves good prediction results. Artificial neural network algorithm is a typical and widely used artificial intelligence forecasting technology. It has strong nonlinear fitting ability and can map any complex nonlinear relationship. The learning rules are simple and easy to implement by computer. However, the neural network also has certain shortcomings, su...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/26
CPCG06Q10/04G06Q50/26Y02P90/84
Inventor 牛东晓戴舒羽浦迪康辉
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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